text stringlengths 14 4.79k | source stringlengths 13 304 | tokens float64 75 1.06k ⌀ | char_length float64 106 4.79k ⌀ | article_title stringlengths 16 300 ⌀ |
|---|---|---|---|---|
Section: Preprocessing of explanatory variables. When a fixed set of nonlinear functions are used to transform the value(s) of a data point, these functions are known as basis functions. An example is polynomial regression, which uses a linear predictor function to fit an arbitrary degree polynomial relationship (up to... | Wikipedia - Linear predictor function - Preprocessing of explanatory variables | 340 | 1,041 | null |
{\displaystyle y_{i}=\beta _{0}+\beta _{1}x_{i}+\beta _{2}x_{i}^{2}+\cdots +\beta _{p}x_{i}^{p}.} In this case, for each data point i, a set of explanatory variables is created as follows: ( x i 1 = x i , x i 2 = x i 2 , … , x i p = x i p ) {\displaystyle (x_{i1}=x_{i},\quad x_{i2}=x_{i}^{2},\quad \ldots ,\quad x_{ip}=... | Wikipedia - Linear predictor function - Preprocessing of explanatory variables | 339 | 778 | null |
{\displaystyle {\boldsymbol {\phi }}(x)=(\phi _{1}(x),\phi _{2}(x),\ldots ,\phi _{p}(x))=(x,x^{2},\ldots ,x^{p}).} This example shows that a linear predictor function can actually be much more powerful than it first appears: It only really needs to be linear in the coefficients. All sorts of non-linear functions of the... | Wikipedia - Linear predictor function - Preprocessing of explanatory variables | 185 | 628 | null |
There is no particular need for the inputs to basis functions to be univariate or single-dimensional (or their outputs, for that matter, although in such a case, a K-dimensional output value is likely to be treated as K separate scalar-output basis functions). An example of this is radial basis functions (RBF's), which... | Wikipedia - Linear predictor function - Preprocessing of explanatory variables | 338 | 969 | null |
A possible usage of RBF's is to create one for every observed data point. This means that the result of an RBF applied to a new data point will be close to 0 unless the new point is near to the point around which the RBF was applied. That is, the application of the radial basis functions will pick out the nearest point... | Wikipedia - Linear predictor function - Preprocessing of explanatory variables | 221 | 1,160 | null |
It is even possible to fit some functions that appear non-linear in the coefficients by transforming the coefficients into new coefficients that do appear linear. For example, a function of the form a + b 2 x i 1 + c x i 2 {\displaystyle a+b^{2}x_{i1}+{\sqrt {c}}x_{i2}} for coefficients a , b , c {\displaystyle a,b,c} ... | Wikipedia - Linear predictor function - Preprocessing of explanatory variables | 309 | 1,102 | null |
Discrete variables referring to more than two possible choices are typically coded using dummy variables (or indicator variables), i.e. separate explanatory variables taking the value 0 or 1 are created for each possible value of the discrete variable, with a 1 meaning "variable does have the given value" and a 0 meani... | Wikipedia - Linear predictor function - Preprocessing of explanatory variables | 329 | 1,566 | null |
Article: Linear separability. In Euclidean geometry, linear separability is a property of two sets of points. This is most easily visualized in two dimensions (the Euclidean plane) by thinking of one set of points as being colored blue and the other set of points as being colored red. These two sets are linearly separa... | Wikipedia - Linear separability - Summary | 177 | 866 | null |
Section: Mathematical definition. Let X 0 {\displaystyle X_{0}} and X 1 {\displaystyle X_{1}} be two sets of points in an n-dimensional Euclidean space. Then X 0 {\displaystyle X_{0}} and X 1 {\displaystyle X_{1}} are linearly separable if there exist n + 1 real numbers w 1 , w 2 , . . , w n , k {\displaystyle w_{1},w_... | Wikipedia - Linear separability - Mathematical definition | 316 | 833 | null |
Section: Number of linear separations. Let T ( N , K ) {\displaystyle T(N,K)} be the number of ways to linearly separate N points (in general position) in K dimensions, then T ( N , K ) = { 2 N K ≥ N 2 ∑ k = 0 K − 1 ( N − 1 k ) K < N {\displaystyle T(N,K)=\left\{{\begin{array}{cc}2^{N}&K\geq N\\2\sum _{k=0}^{K-1}\left(... | Wikipedia - Linear separability - Number of linear separations | 302 | 780 | null |
Section: Linear separability of Boolean functions in n variables. A Boolean function in n variables can be thought of as an assignment of 0 or 1 to each vertex of a Boolean hypercube in n dimensions. This gives a natural division of the vertices into two sets. The Boolean function is said to be linearly separable provi... | Wikipedia - Linear separability - Linear separability of Boolean functions in n variables | 299 | 1,069 | null |
Section: Threshold logic. A linear threshold logic gate is a Boolean function defined by n {\displaystyle n} weights w 1 , … , w n {\displaystyle w_{1},\dots ,w_{n}} and a threshold θ {\displaystyle \theta } . It takes n {\displaystyle n} binary inputs x 1 , … , x n {\displaystyle x_{1},\dots ,x_{n}} , and outputs 1 if... | Wikipedia - Linear separability - Threshold logic | 277 | 892 | null |
Let W ( n ) {\displaystyle W(n)} be the smallest number W {\displaystyle W} such that every possible real threshold function of n {\displaystyle n} variables can be realized using integer weights of absolute value ≤ W {\displaystyle \leq W} . It is known that 1 2 n log n − 2 n + o ( n ) ≤ log 2 W ( n ) ≤ 1 2 n log ... | Wikipedia - Linear separability - Threshold logic | 176 | 479 | null |
Section: Support vector machines. Classifying data is a common task in machine learning. Suppose some data points, each belonging to one of two sets, are given and we wish to create a model that will decide which set a new data point will be in. In the case of support vector machines, a data point is viewed as a p-dime... | Wikipedia - Linear separability - Support vector machines | 206 | 947 | null |
If such a hyperplane exists, it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum margin classifier. More formally, given some training data D {\displaystyle {\mathcal {D}}} , a set of n points of the form D = { ( x i , y i ) ∣ x i ∈ R p , y i ∈ { − 1 , 1 } } i = 1 n {... | Wikipedia - Linear separability - Support vector machines | 301 | 814 | null |
We want to find the maximum-margin hyperplane that divides the points having y i = 1 {\displaystyle y_{i}=1} from those having y i = − 1 {\displaystyle y_{i}=-1} . Any hyperplane can be written as the set of points x {\displaystyle \mathbf {x} } satisfying w ⋅ x − b = 0 , {\displaystyle \mathbf {w} \cdot \mathbf {x} -b... | Wikipedia - Linear separability - Support vector machines | 255 | 862 | null |
Article: Local case-control sampling. In machine learning, local case-control sampling is an algorithm used to reduce the complexity of training a logistic regression classifier. The algorithm reduces the training complexity by selecting a small subsample of the original dataset for training. It assumes the availabilit... | Wikipedia - Local case-control sampling - Summary | 160 | 845 | null |
Section: Imbalanced datasets. In classification, a dataset is a set of N data points ( x i , y i ) i = 1 N {\displaystyle (x_{i},y_{i})_{i=1}^{N}} , where x i ∈ R d {\displaystyle x_{i}\in \mathbb {R} ^{d}} is a feature vector, y i ∈ { 0 , 1 } {\displaystyle y_{i}\in \{0,1\}} is a label. Intuitively, a dataset is imbal... | Wikipedia - Local case-control sampling - Imbalanced datasets | 290 | 1,015 | null |
Section: Algorithm outline. In logistic regression, given the model θ = ( α , β ) {\displaystyle \theta =(\alpha ,\beta )} , the prediction is made according to P ( Y = 1 ∣ X ; θ ) = p ~ θ ( x ) = exp ( α + β T x ) 1 + exp ( α + β T x ) {\displaystyle \mathbb {P} (Y=1\mid X;\theta )={\tilde {p}}_{\theta }(x)={\frac... | Wikipedia - Local case-control sampling - Algorithm outline | 335 | 915 | null |
For a sample ( x , y ) {\displaystyle (x,y)} , define the acceptance probability as a ( x , y ) = | y − p ~ θ ~ ( x ) | {\displaystyle a(x,y)=|y-{\tilde {p}}_{\tilde {\theta }}(x)|} . The algorithm proceeds as follows: Generate independent z i ∼ Bernoulli ( a ( x i , y i ) ) {\displaystyle z_{i}\sim {\text{Bernoulli}}(... | Wikipedia - Local case-control sampling - Algorithm outline | 297 | 667 | null |
Fit a logistic regression model to the subsample S = { ( x i , y i ) : z i = 1 } {\displaystyle S=\{(x_{i},y_{i}):z_{i}=1\}} , obtaining the unadjusted estimates θ ^ S = ( α ^ S , β ^ S ) {\displaystyle {\hat {\theta }}_{S}=({\hat {\alpha }}_{S},{\hat {\beta }}_{S})} . The output model is θ ^ = ( α ^ , β ^ ) {\displays... | Wikipedia - Local case-control sampling - Algorithm outline | 308 | 785 | null |
Section: Algorithm outline > Obtaining the pilot model. In practice, for cases where a pilot model is naturally available, the algorithm can be applied directly to reduce the complexity of training. In cases where a natural pilot is nonexistent, an estimate using a subsample selected through another sampling technique ... | Wikipedia - Local case-control sampling - Algorithm outline > Obtaining the pilot model | 188 | 831 | null |
Article: Lottery ticket hypothesis. In machine learning, the lottery ticket hypothesis is that artificial neural networks with random weights can contain a subnetwork which (entirely by chance) can be tuned to a similar performance as tuning the whole network. The term derived from considering the probability of a tuna... | Wikipedia - Lottery ticket hypothesis - Summary | 195 | 996 | null |
Section: Features. The Lyra codec is designed to transmit speech in real-time when bandwidth is severely restricted, such as over slow or unreliable network connections. It runs at fixed bitrates of 3.2, 6, and 9 kbit/s and it is intended to provide better quality than codecs that use traditional waveform-based algorit... | Wikipedia - Lyra (codec) - Features | 191 | 977 | null |
Section: History. In December 2017, Google researchers published a preprint paper on replacing the Codec 2 decoder with a WaveNet neural network. They found that a neural network is able to extrapolate features of the voice not described in the Codec 2 bitstream and give better audio quality, and that the use of conven... | Wikipedia - Lyra (codec) - History | 288 | 1,299 | null |
Article: M-theory (learning framework). In machine learning and computer vision, M-theory is a learning framework inspired by feed-forward processing in the ventral stream of visual cortex and originally developed for recognition and classification of objects in visual scenes. M-theory was later applied to other areas,... | Wikipedia - M-theory (learning framework) - Summary | 176 | 947 | null |
Section: Intuition > Invariant representations. A great challenge in visual recognition tasks is that the same object can be seen in a variety of conditions. It can be seen from different distances, different viewpoints, under different lighting, partially occluded, etc. In addition, for particular classes objects, suc... | Wikipedia - M-theory (learning framework) - Intuition > Invariant representations | 338 | 1,906 | null |
Section: Intuition > Combining templates and invariant representations. The two ideas outlined in previous sections can be brought together to construct a framework for learning invariant representations. The key observation is how dot product between image I {\displaystyle I} and a template t {\displaystyle t} behaves... | Wikipedia - M-theory (learning framework) - Intuition > Combining templates and invariant representations | 284 | 1,127 | null |
Consider the set of dot products of an image I {\displaystyle I} to all possible transformations of template: { ⟨ I , g ′ t ⟩ ∣ g ′ ∈ G } {\displaystyle \lbrace \langle I,g^{\prime }t\rangle \mid g^{\prime }\in G\rbrace } . If one applies a transformation g {\displaystyle g} to I {\displaystyle I} , the set would becom... | Wikipedia - M-theory (learning framework) - Intuition > Combining templates and invariant representations | 293 | 783 | null |
The set { g − 1 g ′ ∣ g ′ ∈ G } {\displaystyle \lbrace g^{-1}g^{\prime }\mid g^{\prime }\in G\rbrace } is equal to just the set of all elements in G {\displaystyle G} . To see this, note that every g − 1 g ′ {\displaystyle g^{-1}g^{\prime }} is in G {\displaystyle G} due to the closure property of groups, and for every... | Wikipedia - M-theory (learning framework) - Intuition > Combining templates and invariant representations | 337 | 825 | null |
Thus, { ⟨ I , g − 1 g ′ t ⟩ ∣ g ′ ∈ G } = { ⟨ I , g ′ ′ t ⟩ ∣ g ′ ′ ∈ G } {\displaystyle \lbrace \langle I,g^{-1}g^{\prime }t\rangle \mid g^{\prime }\in G\rbrace =\lbrace \langle I,g^{\prime \prime }t\rangle \mid g^{\prime \prime }\in G\rbrace } . One can see that the set of dot products remains the same despite that a... | Wikipedia - M-theory (learning framework) - Intuition > Combining templates and invariant representations | 299 | 1,226 | null |
Section: Theoretical aspects > From orbits to distribution measures. To implement the ideas described in previous sections, one need to know how to derive a computationally efficient invariant representation of an image. Such unique representation for each image can be characterized as it appears by a set of one-dimens... | Wikipedia - M-theory (learning framework) - Theoretical aspects > From orbits to distribution measures | 348 | 1,491 | null |
A natural question arises: how can one compare two orbits? There are several possible approaches. One of them employs the fact that intuitively two empirical orbits are the same irrespective of the ordering of their points. Thus, one can consider a probability distribution P I {\displaystyle P_{I}} induced by the group... | Wikipedia - M-theory (learning framework) - Theoretical aspects > From orbits to distribution measures | 350 | 1,168 | null |
Let K ≥ 2 c ε 2 log n δ {\displaystyle K\geq {\frac {2}{c\varepsilon ^{2}}}\log {\frac {n}{\delta }}} , where c {\displaystyle c} is a universal constant. Then | d ( P I , P I ′ ) − d K ( P I , P I ′ ) | ≤ ε , {\displaystyle |d(P_{I},P_{I}^{\prime })-dK(P_{I},P_{I}^{\prime })|\leq \varepsilon ,} with probability 1 − ... | Wikipedia - M-theory (learning framework) - Theoretical aspects > From orbits to distribution measures | 303 | 768 | null |
This result (informally) says that an approximately invariant and unique representation of an image I {\displaystyle I} can be obtained from the estimates of K {\displaystyle K} 1-D probability distributions P ⟨ I , t k ⟩ {\displaystyle P_{\langle I,t^{k}\rangle }} for k = 1 , … , K {\displaystyle k=1,\ldots ,K} . The ... | Wikipedia - M-theory (learning framework) - Theoretical aspects > From orbits to distribution measures | 303 | 1,072 | null |
To classify an image, the following "recipe" can be used: Memorize a set of images/objects called templates; Memorize observed transformations for each template; Compute dot products of its transformations with image; Compute histogram of the resulting values, called signature of the image; Compare the obtained histogr... | Wikipedia - M-theory (learning framework) - Theoretical aspects > From orbits to distribution measures | 308 | 1,007 | null |
Section: Theoretical aspects > Non-compact groups of transformations. In the "recipe" for image classification, groups of transformations are approximated with finite number of transformations. Such approximation is possible only when the group is compact. Such groups as all translations and all scalings of the image a... | Wikipedia - M-theory (learning framework) - Theoretical aspects > Non-compact groups of transformations | 303 | 1,097 | null |
This subset is called support of ⟨ I , g − 1 t k ⟩ {\displaystyle \langle I,g^{-1}t_{k}\rangle } and denoted as supp ( ⟨ I , g − 1 t k ⟩ ) {\displaystyle \operatorname {supp} (\langle I,g^{-1}t_{k}\rangle )} . It can be proven that if for a transformation g ′ {\displaystyle g^{\prime }} , support set will also lie wi... | Wikipedia - M-theory (learning framework) - Theoretical aspects > Non-compact groups of transformations | 331 | 1,056 | null |
Preferable templates are those with a reasonably small supp ( ⟨ g I , t k ⟩ ) {\displaystyle \operatorname {supp} (\langle gI,t_{k}\rangle )} for a generic image. This property is called localization: templates are sensitive only to images within a small range of transformations. Although minimizing supp ( ⟨ g I , ... | Wikipedia - M-theory (learning framework) - Theoretical aspects > Non-compact groups of transformations | 242 | 1,080 | null |
Section: Theoretical aspects > Non-group transformations. Many interesting transformations of images do not form groups. For instance, transformations of images associated with 3D rotation of corresponding 3D object do not form a group, because it is impossible to define an inverse transformation (two objects may looks... | Wikipedia - M-theory (learning framework) - Theoretical aspects > Non-group transformations | 277 | 1,477 | null |
Section: Theoretical aspects > Hierarchical architectures. The previous section suggests one motivation for hierarchical image recognition architectures. However, they have other benefits as well. Firstly, hierarchical architectures best accomplish the goal of ‘parsing’ a complex visual scene with many objects consisti... | Wikipedia - M-theory (learning framework) - Theoretical aspects > Hierarchical architectures | 350 | 2,033 | null |
For such transformations, the layer should provide covariant rather than invariant, signatures. The property of covariance can be written as distr ( ⟨ μ l ( g I ) , μ l ( t ) ⟩ ) = distr ( ⟨ μ l ( I ) , μ l ( g − 1 t ) ⟩ ) {\displaystyle \operatorname {distr} (\langle \mu _{l}(gI),\mu _{l}(t)\rangle )=\operatorname... | Wikipedia - M-theory (learning framework) - Theoretical aspects > Hierarchical architectures | 234 | 638 | null |
Section: Relation to biology. M-theory is based on a quantitative theory of the ventral stream of visual cortex. Understanding how visual cortex works in object recognition is still a challenging task for neuroscience. Humans and primates are able to memorize and recognize objects after seeing just couple of examples u... | Wikipedia - M-theory (learning framework) - Relation to biology | 345 | 1,778 | null |
Neuroscience suggests that natural functionals for a neuron to compute is a high-dimensional dot product between an "image patch" and another image patch (called template) which is stored in terms of synaptic weights (synapses per neuron). The standard computational model of a neuron is based on a dot product and a thr... | Wikipedia - M-theory (learning framework) - Relation to biology | 217 | 865 | null |
Complex cells are responsible for pooling and computing empirical histograms or statistical moments of it. The following formula for constructing histogram can be computed by neurons: 1 | G | ∑ i = 1 | G | σ ( ⟨ I , g i t k ⟩ + n Δ ) , {\displaystyle {\frac {1}{|G|}}\sum _{i=1}^{|G|}\sigma (\langle I,g_{i}t^{k}\rangle ... | Wikipedia - M-theory (learning framework) - Relation to biology | 169 | 511 | null |
Section: Applications > Applications to computer vision. In authors applied M-theory to unconstrained face recognition in natural photographs. Unlike the DAR (detection, alignment, and recognition) method, which handles clutter by detecting objects and cropping closely around them so that very little background remains... | Wikipedia - M-theory (learning framework) - Applications > Applications to computer vision | 284 | 1,290 | null |
It also performs well on two newer datasets, similar to LFW, but more difficult: significantly jittered (misaligned) version of LFW and SUFR-W (for example, the model's accuracy in the LFW "unaligned & no outside data used" category is 87.55±1.41% compared to state-of-the-art APEM (adaptive probabilistic elastic matchi... | Wikipedia - M-theory (learning framework) - Applications > Applications to computer vision | 316 | 1,401 | null |
Section: Selected articles. J.R. Quinlan (1986). "Induction of Decision Trees". Machine Learning. 1: 81–106. doi:10.1007/BF00116251. Nick Littlestone (1988). "Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm" (PDF). Machine Learning. 2 (4): 285–318. doi:10.1007/BF00116827. John R. An... | Wikipedia - Machine Learning (journal) - Selected articles | 344 | 1,050 | null |
C. de la Higuera (1997). "Characteristic Sets for Grammatical Inference". Machine Learning. 27: 1–14. Robert E. Schapire and Yoram Singer (1999). "Improved Boosting Algorithms Using Confidence-rated Predictions". Machine Learning. 37 (3): 297–336. doi:10.1023/A:1007614523901. Robert E. Schapire and Yoram Singer (2000).... | Wikipedia - Machine Learning (journal) - Selected articles | 348 | 1,053 | null |
"Mathematical Applications of Inductive Logic Programming" (PDF). Machine Learning. 64 (1–3): 25–64. doi:10.1007/s10994-006-8259-x. Will Bridewell and Pat Langley and Ljupco Todorovski and Saso Dzeroski (2008). "Inductive Process Modeling". Machine Learning. Stephen Muggleton and Alireza Tamaddoni-Nezhad (2008). "QG/GA... | Wikipedia - Machine Learning (journal) - Selected articles | 162 | 439 | null |
Section: Types of problems and tasks. Four types of problems are commonly encountered: Control parameter identification: MLC translates to a parameter identification if the structure of the control law is given but the parameters are unknown. One example is the genetic algorithm for optimizing coefficients of a PID con... | Wikipedia - Machine learning control - Types of problems and tasks | 237 | 1,317 | null |
Section: Adaptive Dynamic Programming. Adaptive Dynamic Programming (ADP), also known as approximate dynamic programming or neuro-dynamic programming, is a machine learning control method that combines reinforcement learning with dynamic programming to solve optimal control problems for complex systems. ADP addresses t... | Wikipedia - Machine learning control - Adaptive Dynamic Programming | 313 | 1,182 | null |
ADP employs two interacting components: a critic that estimates the value function V ( x ) ≈ J ( x ) {\displaystyle V(x)\approx J(x)} , and an actor that updates the control policy u ( x ) {\displaystyle u(x)} . The critic and actor are trained iteratively using temporal difference learning or gradient descent to satis... | Wikipedia - Machine learning control - Adaptive Dynamic Programming | 271 | 985 | null |
Article: Machine learning in bioinformatics. Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining. Prior to the emergence of machine learning, bioinformatics algorithms had to be p... | Wikipedia - Machine learning in bioinformatics - Summary | 180 | 962 | null |
Section: Tasks. Machine learning algorithms in bioinformatics can be used for prediction, classification, and feature selection. Methods to achieve this task are varied and span many disciplines; most well known among them are machine learning and statistics. Classification and prediction tasks aim at building models t... | Wikipedia - Machine learning in bioinformatics - Tasks | 190 | 1,091 | null |
Section: Approaches > Feature engineering. The way that features, often vectors in a many-dimensional space, are extracted from the domain data is an important component of learning systems. In genomics, a typical representation of a sequence is a vector of k-mers frequencies, which is a vector of dimension 4 k {\displ... | Wikipedia - Machine learning in bioinformatics - Approaches > Feature engineering | 193 | 812 | null |
Section: Approaches > Classification > Hidden Markov models. Hidden Markov models (HMMs) are a class of statistical models for sequential data (often related to systems evolving over time). An HMM is composed of two mathematical objects: an observed state‐dependent process X 1 , X 2 , … , X M {\displaystyle X_{1},X_{2}... | Wikipedia - Machine learning in bioinformatics - Approaches > Classification > Hidden Markov models | 257 | 1,039 | null |
Section: Approaches > Classification > Convolutional neural networks. Convolutional neural networks (CNN) are a class of deep neural network whose architecture is based on shared weights of convolution kernels or filters that slide along input features, providing translation-equivariant responses known as feature maps.... | Wikipedia - Machine learning in bioinformatics - Approaches > Classification > Convolutional neural networks | 332 | 1,729 | null |
Section: Approaches > Classification > Random forest. Random forests (RF) classify by constructing an ensemble of decision trees, and outputting the average prediction of the individual trees. This is a modification of bootstrap aggregating (which aggregates a large collection of decision trees) and can be used for cla... | Wikipedia - Machine learning in bioinformatics - Approaches > Classification > Random forest | 222 | 1,166 | null |
Section: Approaches > Clustering. Clustering - the partitioning of a data set into disjoint subsets, so that the data in each subset are as close as possible to each other and as distant as possible from data in any other subset, according to some defined distance or similarity function - is a common technique for stat... | Wikipedia - Machine learning in bioinformatics - Approaches > Clustering | 199 | 985 | null |
Section: Approaches > Clustering > Clustering algorithms used in bioinformatics. Data clustering algorithms can be hierarchical or partitional. Hierarchical algorithms find successive clusters using previously established clusters, whereas partitional algorithms determine all clusters at once. Hierarchical algorithms c... | Wikipedia - Machine learning in bioinformatics - Approaches > Clustering > Clustering algorithms used in bioinformatics | 301 | 1,573 | null |
Section: Approaches > Workflow. Typically, a workflow for applying machine learning to biological data goes through four steps: Recording, including capture and storage. In this step, different information sources may be merged into a single set. Preprocessing, including cleaning and restructuring into a ready-to-analy... | Wikipedia - Machine learning in bioinformatics - Approaches > Workflow | 151 | 810 | null |
Section: Applications. In general, a machine learning system can usually be trained to recognize elements of a certain class given sufficient samples. For example, machine learning methods can be trained to identify specific visual features such as splice sites. Support vector machines have been extensively used in can... | Wikipedia - Machine learning in bioinformatics - Applications | 160 | 950 | null |
Section: Applications > Genomics. While genomic sequence data has historically been sparse due to the technical difficulty of sequencing a piece of DNA, the number of available sequences is growing. On average, the number of bases available in the GenBank public repository has doubled every 18 months since 1982. Howeve... | Wikipedia - Machine learning in bioinformatics - Applications > Genomics | 319 | 1,740 | null |
Section: Applications > Proteomics. Proteins, strings of amino acids, gain much of their function from protein folding, where they conform into a three-dimensional structure, including the primary structure, the secondary structure (alpha helices and beta sheets), the tertiary structure, and the quaternary structure. P... | Wikipedia - Machine learning in bioinformatics - Applications > Proteomics | 338 | 1,815 | null |
Section: Applications > Metagenomics. Metagenomics is the study of microbial communities from environmental DNA samples. Currently, limitations and challenges predominate in the implementation of machine learning tools due to the amount of data in environmental samples. Supercomputers and web servers have made access t... | Wikipedia - Machine learning in bioinformatics - Applications > Metagenomics | 333 | 1,661 | null |
developed an algorithm called Ph-CNN to classify data samples from healthy patients and patients with IBD symptoms (to distinguish healthy and sick patients) by using phylogenetic trees and convolutional neural networks. In addition, random forest (RF) methods and implemented importance measures help in the identificat... | Wikipedia - Machine learning in bioinformatics - Applications > Metagenomics | 333 | 1,753 | null |
difficile infection (CDI) and shotgun metagenomics data for human colorectal cancer (CRC). The proposed approach improved the accuracy from 81% to 99.01% for CDI and from 75.14% to 90.17% for CRC. The use of machine learning in environmental samples has been less explored, maybe because of data complexity, especially f... | Wikipedia - Machine learning in bioinformatics - Applications > Metagenomics | 219 | 1,055 | null |
Section: Applications > Microarrays. Microarrays, a type of lab-on-a-chip, are used for automatically collecting data about large amounts of biological material. Machine learning can aid in analysis, and has been applied to expression pattern identification, classification, and genetic network induction. This technolog... | Wikipedia - Machine learning in bioinformatics - Applications > Microarrays | 184 | 999 | null |
Section: Applications > Systems biology. Systems biology focuses on the study of emergent behaviors from complex interactions of simple biological components in a system. Such components can include DNA, RNA, proteins, and metabolites. Machine learning has been used to aid in modeling these interactions in domains such... | Wikipedia - Machine learning in bioinformatics - Applications > Systems biology | 196 | 1,182 | null |
Section: Applications > Text mining. The increase in biological publications increased the difficulty in searching and compiling relevant available information on a given topic. This task is known as knowledge extraction. It is necessary for biological data collection which can then in turn be fed into machine learning... | Wikipedia - Machine learning in bioinformatics - Applications > Text mining | 239 | 1,449 | null |
Section: Applications > Clustering and abundance profiling of biosynthetic gene clusters. Microbial communities are complex assembles of diverse microorganisms, where symbiont partners constantly produce diverse metabolites derived from the primary and secondary (specialized) metabolism, from which metabolism plays an ... | Wikipedia - Machine learning in bioinformatics - Applications > Clustering and abundance profiling of biosynthetic gene clusters | 337 | 1,608 | null |
Section: Applications > Decodification of RiPPs chemical structures. The increase of experimentally characterized ribosomally synthesized and post-translationally modified peptides (RiPPs), together with the availability of information on their sequence and chemical structure, selected from databases such as BAGEL, BAC... | Wikipedia - Machine learning in bioinformatics - Applications > Decodification of RiPPs chemical structures | 181 | 882 | null |
Section: Applications > Mass spectral similarity scoring. Many tandem mass spectrometry (MS/MS) based metabolomics studies, such as library matching and molecular networking, use spectral similarity as a proxy for structural similarity. Spec2vec algorithm provides a new way of spectral similarity score, based on Word2V... | Wikipedia - Machine learning in bioinformatics - Applications > Mass spectral similarity scoring | 169 | 931 | null |
Section: Databases > Bioinformatics analysis for biosynthetic gene clusters > MIBiG. MIBiG, the minimum information about a biosynthetic gene cluster specification, provides a standard for annotations and metadata on biosynthetic gene clusters and their molecular products. MIBiG is a Genomic Standards Consortium projec... | Wikipedia - Machine learning in bioinformatics - Databases > Bioinformatics analysis for biosynthetic gene clusters > MIBiG | 154 | 785 | null |
Section: Databases > Bioinformatics analysis for biosynthetic gene clusters > Open Tree of Life Taxonomy. Open Tree of Life Taxonomy (OTT) aims to build a complete, dynamic, and digitally available Tree of Life by synthesizing published phylogenetic trees along with taxonomic data. Phylogenetic trees have been classifi... | Wikipedia - Machine learning in bioinformatics - Databases > Bioinformatics analysis for biosynthetic gene clusters > Open Tree of Life Taxonomy | 152 | 736 | null |
Article: Machine learning in earth sciences. Applications of machine learning (ML) in earth sciences include geological mapping, gas leakage detection and geological feature identification. Machine learning is a subdiscipline of artificial intelligence aimed at developing programs that are able to classify, cluster, id... | Wikipedia - Machine learning in earth sciences - Summary | 266 | 1,419 | null |
Section: Significance > Complexity of earth science. Problems in earth science are often complex. It is difficult to apply well-known and described mathematical models to the natural environment, therefore machine learning is commonly a better alternative for such non-linear problems. Ecological data are commonly non-l... | Wikipedia - Machine learning in earth sciences - Significance > Complexity of earth science | 199 | 1,150 | null |
Section: Optimal machine learning algorithm. The extensive usage of machine learning in various fields has led to a wide range of algorithms of learning methods being applied. Choosing the optimal algorithm for a specific purpose can lead to a significant boost in accuracy: for example, the lithological mapping of gold... | Wikipedia - Machine learning in earth sciences - Optimal machine learning algorithm | 250 | 1,314 | null |
Section: Usage > Mapping > Geological or lithological mapping and mineral prospectivity mapping. Geological or lithological mapping produces maps showing geological features and geological units. Mineral prospectivity mapping utilizes a variety of datasets such as geological maps and aeromagnetic imagery to produce map... | Wikipedia - Machine learning in earth sciences - Usage > Mapping > Geological or lithological mapping and mineral prospectivity mapping | 288 | 1,548 | null |
Section: Usage > Mapping > Landslide susceptibility and hazard mapping. Landslide susceptibility refers to the probability of landslide of a certain geographical location, which is dependent on local terrain conditions. Landslide susceptibility mapping can highlight areas prone to landslide risks, which is useful for u... | Wikipedia - Machine learning in earth sciences - Usage > Mapping > Landslide susceptibility and hazard mapping | 239 | 1,228 | null |
Section: Usage > Feature identification and detection > Discontinuity analyses. Discontinuities such as fault planes and bedding planes have important implications in civil engineering. Rock fractures can be recognized automatically by machine learning through photogrammetric analysis, even with the presence of interfe... | Wikipedia - Machine learning in earth sciences - Usage > Feature identification and detection > Discontinuity analyses | 188 | 999 | null |
Section: Usage > Feature identification and detection > Carbon dioxide leakage detection. Quantifying carbon dioxide leakage from a geological sequestration site has gained increased attention as the public is interested in whether carbon dioxide is stored underground safely and effectively. Carbon dioxide leakage from... | Wikipedia - Machine learning in earth sciences - Usage > Feature identification and detection > Carbon dioxide leakage detection | 283 | 1,469 | null |
Section: Usage > Feature identification and detection > Quantification of water inflow. The rock mass rating (RMR) system is a widely adopted rock mass classification system by geomechanical means with the input of six parameters. The amount of water inflow is one of the inputs of the classification scheme, representin... | Wikipedia - Machine learning in earth sciences - Usage > Feature identification and detection > Quantification of water inflow | 192 | 985 | null |
Section: Usage > Forecast and predictions > Earthquake early warning systems and forecasting. Earthquake warning systems are often vulnerable to local impulsive noise, therefore giving out false alerts. False alerts can be eliminated by discriminating the earthquake waveforms from noise signals with the aid of ML metho... | Wikipedia - Machine learning in earth sciences - Usage > Forecast and predictions > Earthquake early warning systems and forecasting | 267 | 1,388 | null |
Section: Usage > Forecast and predictions > Streamflow discharge prediction. Real-time streamflow data is integral for decision making (e.g., evacuations, or regulation of reservoir water levels during flooding). Streamflow data can be estimated by data provided by stream gauges, which measure the water level of a rive... | Wikipedia - Machine learning in earth sciences - Usage > Forecast and predictions > Streamflow discharge prediction | 174 | 832 | null |
Section: Challenge > Inadequate training data. An adequate amount of training and validation data is required for machine learning. However, some very useful products like satellite remote sensing data only have decades of data since the 1970s. If one is interested in the yearly data, then only less than 50 samples are... | Wikipedia - Machine learning in earth sciences - Challenge > Inadequate training data | 179 | 961 | null |
Article: Machine learning in physics. Applying machine learning (ML) (including deep learning) methods to the study of quantum systems is an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. Other examples include learning Hamilton... | Wikipedia - Machine learning in physics - Summary | 157 | 875 | null |
Section: Applications of machine learning to physics > Noisy data. The ability to experimentally control and prepare increasingly complex quantum systems brings with it a growing need to turn large and noisy data sets into meaningful information. This is a problem that has already been studied extensively in the classi... | Wikipedia - Machine learning in physics - Applications of machine learning to physics > Noisy data | 220 | 1,303 | null |
Section: Applications of machine learning to physics > Physics discovery and prediction. A deep learning system was reported to learn intuitive physics from visual data (of virtual 3D environments) based on an unpublished approach inspired by studies of visual cognition in infants. Other researchers have developed a ma... | Wikipedia - Machine learning in physics - Applications of machine learning to physics > Physics discovery and prediction | 160 | 942 | null |
Article: Machine learning in video games. Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control, procedural content generation (PCG) and deep learning-based content generation. Machine learning is a subset of artific... | Wikipedia - Machine learning in video games - Summary | 207 | 1,183 | null |
Section: Deep learning agents > StarCraft series > Alphastar. Alphastar was the first AI agent to beat professional StarCraft 2 players without any in-game advantages. The deep learning network of the agent initially received input from a simplified zoomed out version of the gamestate, but was later updated to play usi... | Wikipedia - Machine learning in video games - Deep learning agents > StarCraft series > Alphastar | 187 | 1,009 | null |
Section: Deep learning agents > Strengths and weaknesses of deep learning agents. Machine learning agents are often not covered in many game design courses. Previous use of machine learning agents in games may not have been very practical, as even the 2015 version of AlphaGo took hundreds of CPUs and GPUs to train to a... | Wikipedia - Machine learning in video games - Deep learning agents > Strengths and weaknesses of deep learning agents | 318 | 1,741 | null |
Section: Machine learning for procedural content generation in games. Machine learning has seen research for use in content recommendation and generation. Procedural content generation is the process of creating data algorithmically rather than manually. This type of content is used to add replayability to games withou... | Wikipedia - Machine learning in video games - Machine learning for procedural content generation in games | 260 | 1,408 | null |
Section: Machine learning for procedural content generation in games > Galactic Arms Race. Galactic Arms Race is a space shooter video game that uses neuroevolution powered PCG to generate unique weapons for the player. This game was a finalist in the 2010 Indie Game Challenge and its related research paper won the Bes... | Wikipedia - Machine learning in video games - Machine learning for procedural content generation in games > Galactic Arms Race | 189 | 960 | null |
Section: Deep learning for content generation in games. The introduction of Generative Adversarial Networks first, and then of diffusion models allows for generating in-game content at runtime using non-procedural approaches. Examples include: The 3D printer available in InZOI (available in early access), a life simula... | Wikipedia - Machine learning in video games - Deep learning for content generation in games | 169 | 832 | null |
Article: Machine-learned interatomic potential. Machine-learned interatomic potentials (MLIPs), or simply machine learning potentials (MLPs), are interatomic potentials constructed by machine learning programs. Beginning in the 1990s, researchers have employed such programs to construct interatomic potentials by mappin... | Wikipedia - Machine-learned interatomic potential - Summary | 349 | 1,955 | null |
Almost all neural networks intake atomic coordinates and output potential energies. For some, these atomic coordinates are converted into atom-centered symmetry functions. From this data, a separate atomic neural network is trained for each element; each atomic network is evaluated whenever that element occurs in the g... | Wikipedia - Machine-learned interatomic potential - Summary | 320 | 1,702 | null |
Article: Manifold hypothesis. The manifold hypothesis posits that many high-dimensional data sets that occur in the real world actually lie along low-dimensional latent manifolds inside that high-dimensional space. As a consequence of the manifold hypothesis, many data sets that appear to initially require many variabl... | Wikipedia - Manifold hypothesis - Summary | 267 | 1,457 | null |
Section: The information geometry of statistical manifolds. An empirically-motivated approach to the manifold hypothesis focuses on its correspondence with an effective theory for manifold learning under the assumption that robust machine learning requires encoding the dataset of interest using methods for data compres... | Wikipedia - Manifold hypothesis - The information geometry of statistical manifolds | 224 | 1,295 | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.